24 Human Impact and Mitigation

24.1 Essential Question

How can individuals, communities, and nations reduce their environmental impact?

24.2 Current U.S. Air Quality (Live)

Use this live bubble map to explore current particulate pollution (PM2.5) readings across the United States. Larger, darker circles indicate higher concentrations.

Code

async function safeFetchJson(url) {
    try {
        const response = await fetch(url)
        if (!response.ok) throw new Error(`HTTP ${response.status}`)
        return await response.json()
    } catch (error) {
        return {__error: error.message}
    }
}

us = await safeFetchJson("https://cdn.jsdelivr.net/npm/us-atlas@3/states-10m.json")
states = us?.objects?.states ? topojson.feature(us, us.objects.states) : null

apiUrl = "https://api.openaq.org/v2/latest?country=US&parameter=pm25&limit=500"
proxyUrl = `https://corsproxy.io/?${encodeURIComponent(apiUrl)}`

aq = {
  const data = await safeFetchJson(apiUrl);
  if (!data.__error) return data;
  return await safeFetchJson(proxyUrl);
}

fallbackPoints = [
    {location: "Seattle", city: "Seattle", value: 8, unit: "µg/m³", latitude: 47.61, longitude: -122.33, updated: "sample"},
    {location: "San Francisco", city: "San Francisco", value: 10, unit: "µg/m³", latitude: 37.77, longitude: -122.42, updated: "sample"},
    {location: "Los Angeles", city: "Los Angeles", value: 18, unit: "µg/m³", latitude: 34.05, longitude: -118.24, updated: "sample"},
    {location: "Phoenix", city: "Phoenix", value: 15, unit: "µg/m³", latitude: 33.45, longitude: -112.07, updated: "sample"},
    {location: "Denver", city: "Denver", value: 9, unit: "µg/m³", latitude: 39.74, longitude: -104.99, updated: "sample"},
    {location: "Dallas", city: "Dallas", value: 14, unit: "µg/m³", latitude: 32.78, longitude: -96.80, updated: "sample"},
    {location: "Chicago", city: "Chicago", value: 12, unit: "µg/m³", latitude: 41.88, longitude: -87.63, updated: "sample"},
    {location: "Atlanta", city: "Atlanta", value: 11, unit: "µg/m³", latitude: 33.75, longitude: -84.39, updated: "sample"},
    {location: "Miami", city: "Miami", value: 7, unit: "µg/m³", latitude: 25.76, longitude: -80.19, updated: "sample"},
    {location: "New York", city: "New York", value: 13, unit: "µg/m³", latitude: 40.71, longitude: -74.01, updated: "sample"},
    {location: "Boston", city: "Boston", value: 9, unit: "µg/m³", latitude: 42.36, longitude: -71.06, updated: "sample"},
    {location: "Minneapolis", city: "Minneapolis", value: 8, unit: "µg/m³", latitude: 44.98, longitude: -93.26, updated: "sample"}
]

livePoints = (aq.results || [])
    .map(d => {
        const m = (d.measurements || []).find(x => x.parameter === "pm25")
        const c = d.coordinates || {}
        if (!m || c.latitude == null || c.longitude == null) return null
        return {
            location: d.location,
            city: d.city,
            value: m.value,
            unit: m.unit,
            latitude: c.latitude,
            longitude: c.longitude,
            updated: m.lastUpdated
        }
    })
    .filter(Boolean)

usingFallback = livePoints.length === 0
points = (usingFallback ? fallbackPoints : livePoints).slice(0, maxPoints)

errorMsg = us.__error

errorMsg
    ? html`<div style="padding: 12px; border-left: 4px solid #f44336; background: #ffebee;">
            <strong>Map unavailable:</strong> ${errorMsg}.<br />
            The base map could not be loaded.
        </div>`
    : Plot.plot({
            title: "Live U.S. PM2.5 Air Quality (OpenAQ)",
            subtitle: usingFallback
                ? "Live data blocked — showing sample PM2.5 values"
                : "Bubble size and color indicate PM2.5 concentration (µg/m³)",
            width: 900,
            height: 550,
            projection: "albers-usa",
            color: {scheme: "YlOrRd", legend: true},
            r: {range: [2, 16]},
            marks: [
                Plot.geo(states, {fill: "#f5f5f5", stroke: "#999", strokeWidth: 0.5}),
                Plot.dot(points, {
                    x: "longitude",
                    y: "latitude",
                    r: "value",
                    fill: "value",
                    fillOpacity: 0.7,
                    stroke: "white",
                    strokeWidth: 0.5,
                    tip: true,
                    title: d => `${d.city || ""} ${d.location}\nPM2.5: ${d.value} ${d.unit}\nUpdated: ${d.updated}`
                })
            ]
        })

(a) Interactive 3D model of a volcanic crater.

(b)

Figure 24.1

Figure 24.2: Interactive Wind Rose: Frequency of wind speed and direction.

Figure 24.3: Composite Climate Risk Index by US County (Simulated)

Code

import plotly.graph_objects as go
import pandas as pd
import numpy as np
from urllib.request import urlopen
import json

# Load US county boundaries (GeoJSON)
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
    counties_geojson = json.load(response)

# Generate county-level climate threat data
# This creates realistic patterns based on geographic regions
np.random.seed(42)

# State-level threat patterns (primary threats and their probabilities)
state_patterns = {
    '01': {'threats': ['Hurricane', 'Tornado', 'Flooding'], 'weights': [0.5, 0.3, 0.2]},
    '04': {'threats': ['Extreme Heat', 'Drought', 'Wildfire'], 'weights': [0.5, 0.3, 0.2]},
    '05': {'threats': ['Tornado', 'Flooding', 'Extreme Heat'], 'weights': [0.4, 0.4, 0.2]},
    '06': {'threats': ['Wildfire', 'Drought', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '08': {'threats': ['Drought', 'Wildfire', 'Extreme Heat'], 'weights': [0.45, 0.35, 0.2]},
    '09': {'threats': ['Flooding', 'Winter Storm', 'Hurricane'], 'weights': [0.45, 0.35, 0.2]},
    '10': {'threats': ['Flooding', 'Hurricane', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '11': {'threats': ['Flooding', 'Extreme Heat', 'Winter Storm'], 'weights': [0.5, 0.3, 0.2]},
    '12': {'threats': ['Hurricane', 'Extreme Heat', 'Flooding'], 'weights': [0.6, 0.25, 0.15]},
    '13': {'threats': ['Hurricane', 'Extreme Heat', 'Tornado'], 'weights': [0.5, 0.3, 0.2]},
    '15': {'threats': ['Wildfire', 'Hurricane', 'Drought'], 'weights': [0.4, 0.35, 0.25]},
    '16': {'threats': ['Wildfire', 'Drought', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '17': {'threats': ['Flooding', 'Tornado', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '18': {'threats': ['Flooding', 'Tornado', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '19': {'threats': ['Flooding', 'Tornado', 'Drought'], 'weights': [0.5, 0.3, 0.2]},
    '20': {'threats': ['Drought', 'Tornado', 'Extreme Heat'], 'weights': [0.45, 0.35, 0.2]},
    '21': {'threats': ['Flooding', 'Tornado', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '22': {'threats': ['Hurricane', 'Flooding', 'Extreme Heat'], 'weights': [0.6, 0.25, 0.15]},
    '23': {'threats': ['Winter Storm', 'Flooding', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '24': {'threats': ['Flooding', 'Hurricane', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '25': {'threats': ['Flooding', 'Winter Storm', 'Hurricane'], 'weights': [0.45, 0.35, 0.2]},
    '26': {'threats': ['Flooding', 'Winter Storm', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '27': {'threats': ['Flooding', 'Winter Storm', 'Tornado'], 'weights': [0.45, 0.35, 0.2]},
    '28': {'threats': ['Hurricane', 'Flooding', 'Tornado'], 'weights': [0.5, 0.3, 0.2]},
    '29': {'threats': ['Flooding', 'Tornado', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '30': {'threats': ['Wildfire', 'Drought', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '31': {'threats': ['Drought', 'Tornado', 'Flooding'], 'weights': [0.45, 0.35, 0.2]},
    '32': {'threats': ['Drought', 'Extreme Heat', 'Wildfire'], 'weights': [0.5, 0.3, 0.2]},
    '33': {'threats': ['Winter Storm', 'Flooding', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '34': {'threats': ['Flooding', 'Hurricane', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '35': {'threats': ['Drought', 'Wildfire', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '36': {'threats': ['Flooding', 'Winter Storm', 'Hurricane'], 'weights': [0.45, 0.35, 0.2]},
    '37': {'threats': ['Hurricane', 'Flooding', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '38': {'threats': ['Drought', 'Flooding', 'Extreme Heat'], 'weights': [0.45, 0.35, 0.2]},
    '39': {'threats': ['Flooding', 'Tornado', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '40': {'threats': ['Drought', 'Tornado', 'Extreme Heat'], 'weights': [0.45, 0.35, 0.2]},
    '41': {'threats': ['Wildfire', 'Flooding', 'Drought'], 'weights': [0.5, 0.3, 0.2]},
    '42': {'threats': ['Flooding', 'Winter Storm', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '44': {'threats': ['Flooding', 'Hurricane', 'Winter Storm'], 'weights': [0.5, 0.3, 0.2]},
    '45': {'threats': ['Hurricane', 'Flooding', 'Extreme Heat'], 'weights': [0.6, 0.25, 0.15]},
    '46': {'threats': ['Drought', 'Extreme Heat', 'Flooding'], 'weights': [0.5, 0.3, 0.2]},
    '47': {'threats': ['Flooding', 'Tornado', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '48': {'threats': ['Extreme Heat', 'Drought', 'Hurricane'], 'weights': [0.4, 0.35, 0.25]},
    '49': {'threats': ['Drought', 'Wildfire', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '50': {'threats': ['Winter Storm', 'Flooding', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '51': {'threats': ['Flooding', 'Hurricane', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '53': {'threats': ['Wildfire', 'Flooding', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '54': {'threats': ['Flooding', 'Winter Storm', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '55': {'threats': ['Flooding', 'Winter Storm', 'Tornado'], 'weights': [0.45, 0.35, 0.2]},
    '56': {'threats': ['Drought', 'Wildfire', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]},
    '72': {'threats': ['Hurricane', 'Extreme Heat', 'Flooding'], 'weights': [0.7, 0.2, 0.1]},
}

# Create county-level data from GeoJSON features
counties_data = []
for feature in counties_geojson['features']:
    fips = feature['id']
    state_fips = fips[:2]
    
    # Assign threat based on state pattern
    if state_fips in state_patterns:
        pattern = state_patterns[state_fips]
        threat = np.random.choice(pattern['threats'], p=pattern['weights'])
    else:
        threat = 'Flooding'  # Default for territories/other
    
    counties_data.append({
        'fips': fips,
        'threat': threat
    })

df = pd.DataFrame(counties_data)

# Define color mapping (colorblind-friendly palette)
color_map = {
    'Wildfire': '#d62728',       # Red
    'Hurricane': '#1f77b4',      # Blue  
    'Flooding': '#17becf',       # Cyan
    'Drought': '#bcbd22',        # Yellow-brown
    'Extreme Heat': '#ff7f0e',   # Orange
    'Tornado': '#9467bd',        # Purple
    'Winter Storm': '#7f7f7f'    # Gray
}

# Create the choropleth map
fig = go.Figure(go.Choroplethmapbox(
    geojson=counties_geojson,
    locations=df['fips'],
    z=df['threat'].map({threat: i for i, threat in enumerate(color_map.keys())}),
    colorscale=[[i/(len(color_map)-1), color] for i, color in enumerate(color_map.values())],
    text=df['threat'],
    hovertemplate='<b>County FIPS: %{location}</b><br>' +
                  'Primary Threat: %{text}<br>' +
                  '<extra></extra>',
    marker_opacity=0.8,
    marker_line_width=0.5,
    marker_line_color='white',
    showscale=False
))

# Add custom legend
for i, (threat, color) in enumerate(color_map.items()):
    count = (df['threat'] == threat).sum()
    fig.add_trace(go.Scattermapbox(
        lon=[0], lat=[0],
        mode='markers',
        marker=dict(size=10, color=color),
        name=f'{threat} ({count})',
        showlegend=True,
        hoverinfo='skip'
    ))

# Update layout
fig.update_layout(
    title={
        'text': 'Primary Climate Threats by U.S. County<br><sub>Based on Regional Climate Risk Patterns</sub>',
        'x': 0.5,
        'xanchor': 'center',
        'font': {'size': 22, 'color': '#2c3e50'}
    },
    mapbox=dict(
        style='carto-positron',
        zoom=3,
        center=dict(lat=38, lon=-96)
    ),
    font=dict(size=12, family='Arial, sans-serif'),
    legend=dict(
        title=dict(text='Climate Threat', font=dict(size=14, color='#2c3e50')),
        orientation='v',
        yanchor='middle',
        y=0.5,
        xanchor='left',
        x=1.02,
        bgcolor='rgba(255,255,255,0.9)',
        bordercolor='#cccccc',
        borderwidth=1
    ),
    margin=dict(l=0, r=150, t=80, b=0),
    width=1200,
    height=700
)

fig

Figure 24.4: Primary Climate Threats by U.S. County

24.2.1 ✅ Quick Analysis

Where are the highest PM2.5 readings clustered today?
Which regions show lower PM2.5 values?
What local factors might explain these patterns?

# Human Impact and Mitigation ## Essential Question > How can individuals, communities, and nations reduce their environmental impact? ## Current U.S. Air Quality (Live) Use this live bubble map to explore current particulate pollution (PM2.5) readings across the United States. Larger, darker circles indicate higher concentrations. ```{ojs} //| echo: false Plot = require("@observablehq/plot") d3 = require("d3@7") topojson = require("topojson-client@3") ``` ```{ojs} //| echo: false viewof maxPoints = Inputs.range([50, 500], { step: 50, value: 200, label: "Number of stations to display" }) ``` ```{ojs} //| echo: false async function safeFetchJson(url) { try { const response = await fetch(url) if (!response.ok) throw new Error(`HTTP ${response.status}`) return await response.json() } catch (error) { return {__error: error.message} } } us = await safeFetchJson("https://cdn.jsdelivr.net/npm/us-atlas@3/states-10m.json") states = us?.objects?.states ? topojson.feature(us, us.objects.states) : null apiUrl = "https://api.openaq.org/v2/latest?country=US&parameter=pm25&limit=500" proxyUrl = `https://corsproxy.io/?${encodeURIComponent(apiUrl)}` aq = { const data = await safeFetchJson(apiUrl); if (!data.__error) return data; return await safeFetchJson(proxyUrl); } fallbackPoints = [ {location: "Seattle", city: "Seattle", value: 8, unit: "µg/m³", latitude: 47.61, longitude: -122.33, updated: "sample"}, {location: "San Francisco", city: "San Francisco", value: 10, unit: "µg/m³", latitude: 37.77, longitude: -122.42, updated: "sample"}, {location: "Los Angeles", city: "Los Angeles", value: 18, unit: "µg/m³", latitude: 34.05, longitude: -118.24, updated: "sample"}, {location: "Phoenix", city: "Phoenix", value: 15, unit: "µg/m³", latitude: 33.45, longitude: -112.07, updated: "sample"}, {location: "Denver", city: "Denver", value: 9, unit: "µg/m³", latitude: 39.74, longitude: -104.99, updated: "sample"}, {location: "Dallas", city: "Dallas", value: 14, unit: "µg/m³", latitude: 32.78, longitude: -96.80, updated: "sample"}, {location: "Chicago", city: "Chicago", value: 12, unit: "µg/m³", latitude: 41.88, longitude: -87.63, updated: "sample"}, {location: "Atlanta", city: "Atlanta", value: 11, unit: "µg/m³", latitude: 33.75, longitude: -84.39, updated: "sample"}, {location: "Miami", city: "Miami", value: 7, unit: "µg/m³", latitude: 25.76, longitude: -80.19, updated: "sample"}, {location: "New York", city: "New York", value: 13, unit: "µg/m³", latitude: 40.71, longitude: -74.01, updated: "sample"}, {location: "Boston", city: "Boston", value: 9, unit: "µg/m³", latitude: 42.36, longitude: -71.06, updated: "sample"}, {location: "Minneapolis", city: "Minneapolis", value: 8, unit: "µg/m³", latitude: 44.98, longitude: -93.26, updated: "sample"} ] livePoints = (aq.results || []) .map(d => { const m = (d.measurements || []).find(x => x.parameter === "pm25") const c = d.coordinates || {} if (!m || c.latitude == null || c.longitude == null) return null return { location: d.location, city: d.city, value: m.value, unit: m.unit, latitude: c.latitude, longitude: c.longitude, updated: m.lastUpdated } }) .filter(Boolean) usingFallback = livePoints.length === 0 points = (usingFallback ? fallbackPoints : livePoints).slice(0, maxPoints) errorMsg = us.__error errorMsg ? html`<div style="padding: 12px; border-left: 4px solid #f44336; background: #ffebee;"> Map unavailable: ${errorMsg}. The base map could not be loaded. </div>` : Plot.plot({ title: "Live U.S. PM2.5 Air Quality (OpenAQ)", subtitle: usingFallback ? "Live data blocked — showing sample PM2.5 values" : "Bubble size and color indicate PM2.5 concentration (µg/m³)", width: 900, height: 550, projection: "albers-usa", color: {scheme: "YlOrRd", legend: true}, r: {range: [2, 16]}, marks: [ Plot.geo(states, {fill: "#f5f5f5", stroke: "#999", strokeWidth: 0.5}), Plot.dot(points, { x: "longitude", y: "latitude", r: "value", fill: "value", fillOpacity: 0.7, stroke: "white", strokeWidth: 0.5, tip: true, title: d => `${d.city || ""} ${d.location}\nPM2.5: ${d.value} ${d.unit}\nUpdated: ${d.updated}` }) ] }) ``` ```{python} #| label: fig-volcano #| fig-cap: "Interactive 3D model of a volcanic crater." #| echo: false import plotly.graph_objects as go import numpy as np # Generate Data x = np.linspace(-10, 10, 100) y = np.linspace(-10, 10, 100) X, Y = np.meshgrid(x, y) R = np.sqrt(X**2 + Y**2) Z = 15 * np.exp(-R**2 / 15) - 5 * np.exp(-R**2 / 1) # Plot fig = go.Figure(data=[go.Surface( z=Z, x=x, y=y, colorscale='Earth', contours_z=dict(show=True, usecolormap=True, project_z=True) )]) fig.update_layout( title="3D Volcanic Crater Model", margin=dict(l=0, r=0, b=0, t=40), scene=dict(aspectratio=dict(x=1, y=1, z=0.7)) ) fig ``` ```{python} #| label: fig-windrose #| fig-cap: "Interactive Wind Rose: Frequency of wind speed and direction." #| echo: false import plotly.express as px # 1. Load Built-in Sample Data # This dataframe has columns for direction (N, S, E, W...), strength, and frequency df = px.data.wind() # 2. Create the Polar Bar Chart (Wind Rose) fig = px.bar_polar( df, r="frequency", # Radial axis = how often wind blows this way theta="direction", # Angular axis = direction (N, S, E, W) color="strength", # Color = wind speed category template="plotly_white", color_discrete_sequence=px.colors.sequential.Plasma_r # Scientific color scale ) # 3. Polish Layout for the Textbook fig.update_layout( title="Wind Rose: Wind Speed and Direction Frequency", legend=dict( title="Wind Strength", orientation="h", # Horizontal legend to save vertical space yanchor="bottom", y=-0.3, # Move legend below the chart xanchor="center", x=0.5 ), margin=dict(l=40, r=40, t=60, b=60) # Adjust margins for the legend and title ) fig ``` ```{python} #| label: fig-climate-risk #| fig-cap: "Composite Climate Risk Index by US County (Simulated)" #| echo: false from urllib.request import urlopen import json import pandas as pd import plotly.express as px import numpy as np # 1. Load US County Geometry (The shapes of the counties) # This is a standard public GeoJSON file for US counties with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response: counties = json.load(response) # 2. Generate Simulated "Climate Risk" Data # In a real lab, students would load the FEMA NRI CSV here. fips_codes = [feature['id'] for feature in counties['features']] county_names = [feature['properties'].get('NAME', 'Unknown') for feature in counties['features']] state_names = [feature['properties'].get('STATE', '') for feature in counties['features']] # Define risk levels risk_levels = ['very low', 'low', 'medium', 'high', 'very high', 'no risk'] # Generate comprehensive risk data for each county data = [] for fips, county, state in zip(fips_codes, county_names, state_names): # Assign multiple risk types per county row = { 'fips': fips, 'county': county, 'state': state, 'Wildfire risk': np.random.choice(risk_levels), 'Hurricane risk': np.random.choice(risk_levels), 'Extreme heat risk': np.random.choice(risk_levels), 'Extreme rainfall risk': np.random.choice(risk_levels), 'Sea level rise risk': np.random.choice(risk_levels), 'Water stress risk': np.random.choice(risk_levels) } # Determine dominant (highest) risk for coloring risks = [ ('Wildfire', row['Wildfire risk']), ('Hurricane', row['Hurricane risk']), ('Extreme heat', row['Extreme heat risk']), ('Extreme rainfall', row['Extreme rainfall risk']), ('Sea level rise', row['Sea level rise risk']), ('Water stress', row['Water stress risk']) ] # Filter out "no risk" and pick the first non-no-risk type active_risks = [r for r in risks if r[1] != 'no risk'] row['dominant_risk'] = active_risks[0][0] if active_risks else 'Low risk' data.append(row) df = pd.DataFrame(data) # Create custom hover template df['hover_text'] = df.apply(lambda row: f"{row['county']}, {row['state']} " + f" ".join([f"{k}: {row[k]}" for k in ['Wildfire risk', 'Hurricane risk', 'Extreme heat risk', 'Extreme rainfall risk', 'Sea level rise risk', 'Water stress risk'] if row[k] != 'no risk']), axis=1) # 3. Create the Map (color-coded by risk type with muted colors) fig = px.choropleth( df, geojson=counties, locations='fips', color='dominant_risk', color_discrete_map={ 'Wildfire': '#e8a598', # Muted coral/pink 'Hurricane': '#7fb8b3', # Muted teal/green 'Extreme heat': '#f4c49a', # Muted peach/orange 'Extreme rainfall': '#82b4d9', # Muted blue 'Sea level rise': '#d4a5d8', # Muted purple 'Water stress': '#f5d999', # Muted yellow/tan 'Low risk': '#f0f0f0' # Light gray }, scope="usa", hover_name='county', custom_data=['hover_text'], labels={'dominant_risk': 'Primary Risk'} ) # 4. Polish for Textbook fig.update_traces( hovertemplate='%{customdata[0]}<extra></extra>' ) fig.update_layout( margin={"r":0,"t":30,"l":0,"b":0}, title_text="Projected Climate Vulnerability by Risk Type (US Counties)", title_x=0.5, geo=dict( showlakes=True, lakecolor='rgb(255, 255, 255)', bgcolor='rgba(0,0,0,0)' ), legend=dict( title_text='Primary Risk Type', orientation="v", yanchor="middle", y=0.5, xanchor="left", x=1.02 ) ) fig ``` ``` {python} #| label: fig-climate-threats-county #| fig-cap: "Primary Climate Threats by U.S. County" #| fig-width: 12 #| fig-height: 8 import plotly.graph_objects as go import pandas as pd import numpy as np from urllib.request import urlopen import json # Load US county boundaries (GeoJSON) with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response: counties_geojson = json.load(response) # Generate county-level climate threat data # This creates realistic patterns based on geographic regions np.random.seed(42) # State-level threat patterns (primary threats and their probabilities) state_patterns = { '01': {'threats': ['Hurricane', 'Tornado', 'Flooding'], 'weights': [0.5, 0.3, 0.2]}, '04': {'threats': ['Extreme Heat', 'Drought', 'Wildfire'], 'weights': [0.5, 0.3, 0.2]}, '05': {'threats': ['Tornado', 'Flooding', 'Extreme Heat'], 'weights': [0.4, 0.4, 0.2]}, '06': {'threats': ['Wildfire', 'Drought', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '08': {'threats': ['Drought', 'Wildfire', 'Extreme Heat'], 'weights': [0.45, 0.35, 0.2]}, '09': {'threats': ['Flooding', 'Winter Storm', 'Hurricane'], 'weights': [0.45, 0.35, 0.2]}, '10': {'threats': ['Flooding', 'Hurricane', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '11': {'threats': ['Flooding', 'Extreme Heat', 'Winter Storm'], 'weights': [0.5, 0.3, 0.2]}, '12': {'threats': ['Hurricane', 'Extreme Heat', 'Flooding'], 'weights': [0.6, 0.25, 0.15]}, '13': {'threats': ['Hurricane', 'Extreme Heat', 'Tornado'], 'weights': [0.5, 0.3, 0.2]}, '15': {'threats': ['Wildfire', 'Hurricane', 'Drought'], 'weights': [0.4, 0.35, 0.25]}, '16': {'threats': ['Wildfire', 'Drought', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '17': {'threats': ['Flooding', 'Tornado', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '18': {'threats': ['Flooding', 'Tornado', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '19': {'threats': ['Flooding', 'Tornado', 'Drought'], 'weights': [0.5, 0.3, 0.2]}, '20': {'threats': ['Drought', 'Tornado', 'Extreme Heat'], 'weights': [0.45, 0.35, 0.2]}, '21': {'threats': ['Flooding', 'Tornado', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '22': {'threats': ['Hurricane', 'Flooding', 'Extreme Heat'], 'weights': [0.6, 0.25, 0.15]}, '23': {'threats': ['Winter Storm', 'Flooding', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '24': {'threats': ['Flooding', 'Hurricane', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '25': {'threats': ['Flooding', 'Winter Storm', 'Hurricane'], 'weights': [0.45, 0.35, 0.2]}, '26': {'threats': ['Flooding', 'Winter Storm', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '27': {'threats': ['Flooding', 'Winter Storm', 'Tornado'], 'weights': [0.45, 0.35, 0.2]}, '28': {'threats': ['Hurricane', 'Flooding', 'Tornado'], 'weights': [0.5, 0.3, 0.2]}, '29': {'threats': ['Flooding', 'Tornado', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '30': {'threats': ['Wildfire', 'Drought', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '31': {'threats': ['Drought', 'Tornado', 'Flooding'], 'weights': [0.45, 0.35, 0.2]}, '32': {'threats': ['Drought', 'Extreme Heat', 'Wildfire'], 'weights': [0.5, 0.3, 0.2]}, '33': {'threats': ['Winter Storm', 'Flooding', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '34': {'threats': ['Flooding', 'Hurricane', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '35': {'threats': ['Drought', 'Wildfire', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '36': {'threats': ['Flooding', 'Winter Storm', 'Hurricane'], 'weights': [0.45, 0.35, 0.2]}, '37': {'threats': ['Hurricane', 'Flooding', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '38': {'threats': ['Drought', 'Flooding', 'Extreme Heat'], 'weights': [0.45, 0.35, 0.2]}, '39': {'threats': ['Flooding', 'Tornado', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '40': {'threats': ['Drought', 'Tornado', 'Extreme Heat'], 'weights': [0.45, 0.35, 0.2]}, '41': {'threats': ['Wildfire', 'Flooding', 'Drought'], 'weights': [0.5, 0.3, 0.2]}, '42': {'threats': ['Flooding', 'Winter Storm', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '44': {'threats': ['Flooding', 'Hurricane', 'Winter Storm'], 'weights': [0.5, 0.3, 0.2]}, '45': {'threats': ['Hurricane', 'Flooding', 'Extreme Heat'], 'weights': [0.6, 0.25, 0.15]}, '46': {'threats': ['Drought', 'Extreme Heat', 'Flooding'], 'weights': [0.5, 0.3, 0.2]}, '47': {'threats': ['Flooding', 'Tornado', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '48': {'threats': ['Extreme Heat', 'Drought', 'Hurricane'], 'weights': [0.4, 0.35, 0.25]}, '49': {'threats': ['Drought', 'Wildfire', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '50': {'threats': ['Winter Storm', 'Flooding', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '51': {'threats': ['Flooding', 'Hurricane', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '53': {'threats': ['Wildfire', 'Flooding', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '54': {'threats': ['Flooding', 'Winter Storm', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '55': {'threats': ['Flooding', 'Winter Storm', 'Tornado'], 'weights': [0.45, 0.35, 0.2]}, '56': {'threats': ['Drought', 'Wildfire', 'Extreme Heat'], 'weights': [0.5, 0.3, 0.2]}, '72': {'threats': ['Hurricane', 'Extreme Heat', 'Flooding'], 'weights': [0.7, 0.2, 0.1]}, } # Create county-level data from GeoJSON features counties_data = [] for feature in counties_geojson['features']: fips = feature['id'] state_fips = fips[:2] # Assign threat based on state pattern if state_fips in state_patterns: pattern = state_patterns[state_fips] threat = np.random.choice(pattern['threats'], p=pattern['weights']) else: threat = 'Flooding' # Default for territories/other counties_data.append({ 'fips': fips, 'threat': threat }) df = pd.DataFrame(counties_data) # Define color mapping (colorblind-friendly palette) color_map = { 'Wildfire': '#d62728', # Red 'Hurricane': '#1f77b4', # Blue 'Flooding': '#17becf', # Cyan 'Drought': '#bcbd22', # Yellow-brown 'Extreme Heat': '#ff7f0e', # Orange 'Tornado': '#9467bd', # Purple 'Winter Storm': '#7f7f7f' # Gray } # Create the choropleth map fig = go.Figure(go.Choroplethmapbox( geojson=counties_geojson, locations=df['fips'], z=df['threat'].map({threat: i for i, threat in enumerate(color_map.keys())}), colorscale=[[i/(len(color_map)-1), color] for i, color in enumerate(color_map.values())], text=df['threat'], hovertemplate='County FIPS: %{location} ' + 'Primary Threat: %{text} ' + '<extra></extra>', marker_opacity=0.8, marker_line_width=0.5, marker_line_color='white', showscale=False )) # Add custom legend for i, (threat, color) in enumerate(color_map.items()): count = (df['threat'] == threat).sum() fig.add_trace(go.Scattermapbox( lon=[0], lat=[0], mode='markers', marker=dict(size=10, color=color), name=f'{threat} ({count})', showlegend=True, hoverinfo='skip' )) # Update layout fig.update_layout( title={ 'text': 'Primary Climate Threats by U.S. County Based on Regional Climate Risk Patterns', 'x': 0.5, 'xanchor': 'center', 'font': {'size': 22, 'color': '#2c3e50'} }, mapbox=dict( style='carto-positron', zoom=3, center=dict(lat=38, lon=-96) ), font=dict(size=12, family='Arial, sans-serif'), legend=dict( title=dict(text='Climate Threat', font=dict(size=14, color='#2c3e50')), orientation='v', yanchor='middle', y=0.5, xanchor='left', x=1.02, bgcolor='rgba(255,255,255,0.9)', bordercolor='#cccccc', borderwidth=1 ), margin=dict(l=0, r=150, t=80, b=0), width=1200, height=700 ) fig ``` ::: {.check-understanding} ### ✅ Quick Analysis 1. Where are the highest PM2.5 readings clustered today? 2. Which regions show lower PM2.5 values? 3. What local factors might explain these patterns? :::